CBF-LLM: Safe Control for LLM Alignment

This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the safety filter, designed based on the CBF, to the output generation of the baseline LLM, i.e., the sequence of the token, with the aim of intervening in the generated text. The overall text-generation system is implemented with Llama 3 and a RoBERTa model, and the source code is available at https://github.com/Mya-Mya/CBF-LLM. The experiment demonstrates its control ability and effectiveness in reducing the number of interventions needed for user-specified alignment tasks.

💡 Research Summary

The paper introduces a novel safety‑oriented alignment framework for large language models (LLMs) called CBF‑LLM, which leverages the concept of Control Barrier Functions (CBFs) from control theory to intervene directly in the token generation process. Unlike conventional alignment methods such as Reinforcement Learning from Human Feedback (RLHF) or Supervised Fine‑Tuning (SFT) that modify the internal parameters of the LLM, CBF‑LLM treats the LLM as a black‑box predictor and adds an external safety filter between the token probability distribution and the token selection step. This “learning‑free” approach enables the same filter to be applied to any underlying LLM without retraining.

The authors first review the mathematical foundation of CBFs. In a continuous‑time dynamical system ˙x = g(x,u), a scalar barrier function h(x) defines a safe set S = {x | h(x) ≥ 0}. The CBF condition ˙h(x) ≥ –α h(x) (with α ∈